Educational Data Mining for Secondary and Higher Secondary

Educational Data Mining for Secondary and Higher Secondary Education in Bangladesh Md. Shafiqul Islam d A typical implementation of our data warehouse schema is illustrated in the figure below. A three-dimensional data cube (3 D Cube), is generated from data warehouse, which should look graphically like the following diagram. The data here is grouped into individual cubes according to Location, Group and Time. Any relevant data cube can be generated from the data available in the data warehouse, similarly. These cubes should be observed to get information of educational records. Sylhet Khulna Chittagong Rajshahi Dinajpur Barishal Comilla Dhaka Objective Methodology The first phase of the EDM process is to discover relationships among data. This involves searching through a repository of data from an educational environment with the goal of finding consistent relationships between variables. Our designed data warehouse repository is shown below. Since both the repositories for SSC and HSC are almost similar, here is only the schema for SSC is shown for simplicity. Time Dimension Table Fact_all Session_start (pk) session_end passing_year Dimension Table Location Institution_code (pk) Student_key (fk) Session_start (fk) Subject_key (fk) Student_key (pk) Roll Reg birth_day Institution_code(fk) birth_month grade_point birth_year sex group institution thana tio ca Lo Humanity Science Business Studies 2014 2010 2013 2011 2009 2012 2008 2005 2004 2006 2007 2003 2002 2001 Time (Year) Our objective is to design and implement an Educational Data Warehouse repository, which may further be used to extract useful information for Knowledge Discovery from Data for educational data records (KDD). We have only focused over the two public examination: one is Secondary School Certificate (SSC) and the other is Higher Secondary School Certificate (HSC) examination, in Bangladesh. Dimension Table Group Educational Data Mining refers to the techniques, tools, and researches, designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. n (B oa Educational data is one of the huge resources of big data in current world. This data record for our country is a great source to extract information about our educational process, progress and also an essential source of elements to predict about the future learning behavior. But these utilities cannot be gained merely looking over the raw data of education. A proper procedure of analysis is the prerequisite to get valuable information from these raw data, which is known as Educational Data Mining (EDM). Implementations rd ) Introduction Dimension Table area/district Subject sub_area/division Subject_key (pk) centre subject_codes subject_numbers Figure-1 : Star Schema of SSC/HSC Data Warehouse. Figure-2: A 3 -D data cube representation of SSC/HSC data according to Time, Location and Group. The result is shown with a time domain of [2001 -2014]. Though Madrasha and Technical board does not shown, the corresponding result could be gain accordingly. Expected Findings Although educational data both in SSC and HSC are extensive, these are much precise than many other source of big data to examine for mining. Using the above schema, we can perform the following tasks. ü Analyzing and visualization of student data ü Grouping students according to specific properties ü Detecting undesirable student behaviors ü Predicting student performance The distilled data can also be used to plot into curve, bar chart or into statistical regression analysis for human judgment. Conclusion In this thesis, we have focused on designing a data warehouse to make the knowledge discovery from educational data available for secondary and higher secondary school examination data sources more easy. Since the data is extensive and increasing with time, challenges are associated with implementing educational data mining. As a developing country Bangladesh also needs to pay attention over the hidden information exists into educational data resources for educational progress and success. References 1. Data Mining Concepts and Techniques(Third Edition) - by Jiawei Han, Micheline Kamber, Jian Pei 2. http: //www. educationaldatamining. org 3. http: //en. wikipedia. org/wiki/Educational_data_mining 4. http: //www. educationboard. gov. bd Department of Computer Science and Engineering (CSE), BUET